• International Journal of Vascular Biomedical Engineering
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• v.4 no.1
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• pp.27-30
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• 2006

Background; Pleural micro-metastasis of lung cancer is detected by touch print cytology or pleural lavage cytology, but its prognostic impact has not elucidated yet. We hypothesize that recurrence may depend on the amount of tumor cells disseminated in pleural cavity, if the invasiveness of all cancer is the same. To predict the amount of tumor cells disseminated in pleural cavity, we need pleural surface area, distributed pattern of cells and concentration of cells per unit area. Human pleural surface area has not reported yet. In this report, we calculate the normal human pleural surface area using CT image data processing. Methods; Twenty persons were checked CT scan, and we obtained the data from each image. In order to calculate the pleural surface, the outline of lung was firstly extruded from CT image data using home-made Digitizer program. And the distance between CT images was calculated from the extruded outline. Finally a normal human pleural surface was calculated from function between the distance of consecutive CT images and the calculated length. Results; Their mean age is $65{\pm}12$ years old (range $26{\sim}77$), body weight is $62{\pm}9\;kg\;(48{\sim}80)$, and height is $167{\pm}6\;cm\;(156{\sim}176)$. The number of images used is $36{\pm}7\;(24{\sim}51)$. Pleural surface area is $211,888{\pm}35,756\;mm^2\;(143,880{\sim}279,576)$. Right-side pleural surface area is $107,932\;mm^2$ and Lt is $103,955\;mm^2$. Costal, mediastinal and diaphragmatic surfaces of right-side pleura are $77,483\;mm^2,\;39,057\;mm^2,\;and\;8,608\;mm^2$ respectively, and left-side are $72,497\;mm^2,\;35,578\;mm^2,\;and\;4,120\;mm^2$ respectively. Conclusion; Normal human pleural surface area is calculated using CT image data at first and the result is about $0.212\;m^2$.

• Journal of the Korean Geotechnical Society
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• v.27 no.11
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• pp.93-101
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• 2011

High quality X-ray computed microtomography (micro-CT) imaging of internal microstructures and pore space in geomaterials is often hampered by some inherent noises embedded in the images. In this paper, we introduce image calibration techniques for removing the most common noises in X-ray micro-CT, cupping (brightness difference between the periphery and central regions) and ring artifacts (consecutive concentric circles emanating from the origin). The artifacts removal sequentially applies coordinate transformation, normalization, and low-pass filtering in 2D Fourier spectrum to raw CT-images. The applicability and performance of the techniques are showcased by describing extraction of 3D pore structures from micro-CT images of porous basalt using artifacts reductions, binarization, and volume stacking. Comparisions between calibrated and raw images indicate that the artifacts removal allows us to avoid the overestimation of porosity of imaged materials, and proper calibration of the artifacts plays a crucial role in using X-ray CT for geomaterials.

• Radiation Oncology Journal
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• v.16 no.1
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• pp.81-86
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• 1998

Purpose : To generate the axial, coronal and sagittal images from conventional simulation images, as a preliminary study of broad-beam simulator CT. Methods and Materials : Volumetric filtered back-projection was performed using 90 sheets of films from conventional simulator for every $4^{\circ}$ gantry angle. Two mAs exposure condition for 120kvp beam qualify at SFD 140cm was given to each film. Outside the silhouette portion was removed and scatter component was deconvolved before back-projection. Results : The axial, the sagittal and the coronal images with same spatial resolutions over all direction could be obtained. But image quality was very poor. Conclusion : CT images could be obtained using broad-beam. Scatter deconvolution technique was effective for this reconstruction. The fact that same spatial resolutions over all direction tells us the possibility of application of this technique to DRR or Simulator-CT. But the quality of image should be improved for clinical application practically.

• Journal of radiological science and technology
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• v.44 no.4
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• pp.367-373
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• 2021

A quality assurance of computed tomography(CT) have done seven items that were water attenuation coefficient, noise, homogeneity, spatial resolution, contrast resolution, slice thickness, artifact using by standard phantom. But there is no quality assurance items and methods for CT simulator at domestic institutions yet. Therefore the study aimed to access the CT dose index(CTDI), table tilting, image distortion, laser accuracy, table movement accuracy and CT seven items for CT simulator quality assurance. The CTDI at the center of the head phantom was 0.81 for 80 kVp, 1.55 for 100 kVp, 2.50 for 120 mm, 0.22 for 80 kVp at the center of the body phantom, 0.469 for 100 kVp, and 0.81 for 120 kVp. The table tilting was within the tolerance range of ±1.0° or less. Image distortion had 1 mm distortion in the left and right images based on the center, and the laser accuracy was measured within ±2 mm tolerance. The purpose of this study is to improve the quality assurance items suitable for the current situation in Korea in order to protect the normal tissues during the radiation treatment process and manage the CT simulator that is implemented to find the location of the tumor more clearly. In order to improve the accuracy of the CT simulator when looking at the results, the error range of each item should be small. It is hoped that the quality assurance items of the CT simulator will be improved by suggesting the quality assurance direction of the CT simulator in this study, and the results of radiation therapy will also improve.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.601-606
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• 2006

The image data amount that used in medical institution with great development of medical technology is increasing rapidly. Therefore, people need automation method that use image processing description than macrography of doctors for analysis many medical image. In this paper. we propose that acquire texture information to using GLCM about liver area of abdomen CT image, and automatically detects liver tumor using PCA from this data. Method by one feature as intensity of existent liver humor detection was most but we changed into 4 principal component accumulation images using GLCM's texture information 8 feature. Experiment result, 4 principal component accumulation image's variance percentage is 89.9%. It was seen this compare with liver tumor detecting that use only intensity about 92%. This means that can detect liver tumor even if reduce from dimension of image data to 4 dimensions that is the half in 8 dimensions.

• Korean Journal of Radiology
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• v.19 no.6
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• pp.1031-1041
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• 2018

Objective: To compare image quality and radiation dose of high-pitch dual-source spiral cardiothoracic computed tomography (CT) between non-electrocardiography (ECG)-synchronized and prospectively ECG-triggered data acquisitions in young children with congenital heart disease. Materials and Methods: Eighty-six children (${\leq}3$ years) with congenital heart disease who underwent high-pitch dual-source spiral cardiothoracic CT were included in this retrospective study. They were divided into two groups (n = 43 for each; group 1 with non-ECG-synchronization and group 2 with prospective ECG triggering). Patient-related parameters, radiation dose, and image quality were compared between the two groups. Results: There were no significant differences in patient-related parameters including age, cross-sectional area, body density, and water-equivalent area between the two groups (p > 0.05). Regarding radiation dose parameters, only volume CT dose index values were significantly different between group 1 ($1.13{\pm}0.09mGy$) and group 2 ($1.07{\pm}0.12mGy$, p < 0.02). Among image quality parameters, significantly higher image noise ($3.8{\pm}0.7$ Hounsfield units [HU] vs. $3.3{\pm}0.6HU$, p < 0.001), significantly lower signal-to-noise ratio ($105.0{\pm}28.9$ vs. $134.1{\pm}44.4$, p = 0.001) and contrast-to-noise ratio ($84.5{\pm}27.2$ vs. $110.1{\pm}43.2$, p = 0.002), and significantly less diaphragm motion artifacts ($3.8{\pm}0.5$ vs. $3.7{\pm}0.4$, p < 0.04) were found in group 1 compared with group 2. Image quality grades of cardiac structures, coronary arteries, ascending aorta, pulmonary trunk, lung markings, and chest wall showed no significant difference between groups (p > 0.05). Conclusion: In high-pitch dual-source spiral pediatric cardiothoracic CT, additional ECG triggering does not substantially reduce motion artifacts in young children with congenital heart disease.

• Advances in nano research
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• v.12 no.2
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• Journal of Internet of Things and Convergence
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• v.10 no.3
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• pp.25-33
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• 2024

In tomographic image reconstruction, the focus is on developing CT image reconstruction methods that can maintain high image quality while reducing patient radiation exposure. Typically, statistical image reconstruction methods have the ability to generate high-quality and accurate images while significantly reducing patient radiation exposure. However, in cases like CT image reconstruction, which involve multi-dimensional parameter estimation, the degree of the Hessian matrix of the penalty function is very large, making it impossible to calculate. To solve this problem, the author proposed the PEMG-1 algorithm. However, the PEMG-1 algorithm has issues with the convergence speed, which is typical of statistical image reconstruction methods, and increasing the penalty log-likelihood. In this study, we propose a reconstruction algorithm that ensures fast convergence speed and monotonic increase in likelihood. The basic structure of this algorithm involves sequentially updating groups of pixels instead of updating all parameters simultaneously with each iteration.

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.67-71
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• 2013

Purpose: The Change of CT exposure condition have a effect on image quality and patient exposure dose. In this study, we evaluated effect CT image quality and SUV when CT parameters (Pitch, Rotation time) were changed. Materials and Methods: Discovery Ste (GE, USA) was used as a PET/CT scanner. Using GE QA Phantom and AAPM CT Performance Phantom for evaluate Noise of CT image. Images are acquired by using 24 combinations that four stages pitch (0.562, 0.938, 1.375, 1.75:1) and six stages X-ray tube rotation time (0.5s-1.0s). PET images are acquired using 1994 NEMA PET Phantom ($^{18}F-FDG$ 5.3 kBq/mL, 2.5 min/frame). For noise test, noise are evaluated by standard deviation of each image's CT numbers. And then we used expectation noise according to change of DLP (Dose Length Product) to experimental noise ratio for index of effectiveness. For spatial resolution test, we confirmed that it is possible to identify to 1.0 mm size of the holes at the AAPM CT Performance Phantom. Finally we evaluated each 24 image's SUV. Results: Noise efficiency were 1.00, 1.03, 1.01, 0.96 and 1.00, 1.04, 1.02, 0.97 when pitch changes at the QA Phantom and AAPM Phantom. In case of X-ray tube rotation time changes, 0.99, 1.02, 1.00, 1.00, 0.99, 0.99 and 1.01, 1.01, 0.99, 1.01, 1.01, 1.01 at the QA Phantom and AAPM Phantom. We could identify 1.0 mm size of the holes all 24 images. Also, there were no significant change of SUV and all image's average SUV were 1.1. Conclusion: 1.75:1 pitch is the most effective value at the CT image evaluation according to pitch change and It doesn't affect to the spatial resolution and SUV. However, the change of rotation time doesn't affect anything. So, we recommend to use the effective pitch like 1.75:1 and adequate X-ray tube rotation time according to patient size.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.